System and method for providing content based on knowledge graph

ABSTRACT

Provided are an artificial intelligence (AI) system using a machine learning algorithm and an application of the AI system. A device for providing content based on a knowledge graph includes: a memory storing instructions; and a processor configured to execute the instructions to: obtain context information related to the device; obtain a first device knowledge graph of a user of the device by inputting the obtained context information to a first AI model for determining a relation between entities related to the user of the device; request, from a server, a server knowledge graph generated by the server; receive the server knowledge graph; obtain a second device knowledge graph of the user by inputting the obtained first device knowledge graph and the received server knowledge graph to a second AI model for extending the first device knowledge graph; and provide content based on the obtained second device knowledge graph.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. § 119to Korean Patent Application No. 10-2018-0123272, filed on Oct. 16,2018, in the Korean Intellectual Property Office, the disclosure ofwhich is incorporated by reference herein in its entirety.

BACKGROUND 1. Field

The disclosure relates to systems and methods for providing contentbased on a knowledge graph, and more particularly, systems and methodsfor providing content based on a knowledge graph considering a user'sprivacy.

2. Description of Related Art

An artificial intelligence (AI) system is a computer system configuredto get smarter by training itself and making determinationsspontaneously, unlike an existing rule-based smart system. Because arecognition rate of the AI system improves and the AI system moreaccurately understands a user's taste as the AI system is increasinglyused, the rule-based smart system is being gradually replaced by a deeplearning-based AI system.

AI technology includes machine learning (e.g., deep learning) andelement technologies using the machine learning. Machine learning is analgorithm technology that self-classifies/learns characteristics ofinput data, and element technologies are technologies using a machinelearning algorithm such as deep learning and include technical fieldssuch as linguistic understanding, visual understanding,inference/prediction, knowledge representation, and motion control.

Various fields to which AI technology is applied are as follows.Linguistic understanding is a technology for recognizing andapplying/processing human languages/characters and includes naturallanguage processing, machine translation, dialog systems, questionanswering, and voice recognition/synthesis. Visual understanding is atechnology for recognizing and processing objects like a human visualsystem and includes object recognition, object tracking, imagesearching, person recognition, scene understanding, spatialunderstanding, and image enhancement. Inference/prediction is atechnology for judging information and logically inferring andpredicting the same and includes knowledge/probability-based reasoning,optimization prediction, preference-based planning, and recommendation.Knowledge representation is an automation technology for incorporatinghuman experience information into knowledge data and includes knowledgebuilding (e.g., data generation/classification), and knowledgemanagement (e.g., data utilization). Motion control is a technology forcontrolling self-driving of autonomous vehicles and the motion of robotsand includes movement control (e.g., navigation, collision avoidance, ordriving), and manipulation control (e.g., behavior control).

Furthermore, there is a demand for AI technology for protecting a user'sprivacy and effectively extending a knowledge graph related to the user.

SUMMARY

Provided are a system and method for obtaining and using a knowledgegraph related to a user by using a plurality of artificial intelligence(AI) models.

Also provided are a system and method for protecting individual privacyand obtaining and using a knowledge graph related to a user.

Furthermore, provided are a system and method for effectively extendinga knowledge graph related to a user and providing recommended content tothe user through cooperation between a device and a server.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

In accordance with an aspect of the disclosure, a device for providingcontent based on a knowledge graph includes: a device for providingcontent based on a knowledge graph, the device including: acommunication interface; a memory storing one or more instructions; anda processor configured to execute the one or more instructions to:obtain context information related to the device; obtain a first deviceknowledge graph of a user of the device by inputting the obtainedcontext information to a first artificial intelligence (AI) model fordetermining a relation between entities related to the user of thedevice; control to request, from a server, a server knowledge graphgenerated by the server; control to receive the server knowledge graphfrom the server; obtain a second device knowledge graph of the user byinputting the obtained first device knowledge graph and the receivedserver knowledge graph to a second AI model for extending the firstdevice knowledge graph; and provide content based on the obtained seconddevice knowledge graph.

Each of the first AI model and the second AI model may be an AI modeltrained by using, as an AI algorithm, at least one of a machine learningalgorithm, a neural network algorithm, a genetic algorithm, a deeplearning algorithm, or a classification algorithm.

The server knowledge graph may be generated by the server based on bigdata provided to the server by the device and at least one other device.

The processor may be further configured to execute the one or moreinstructions to process the context information into text indicatingsequential operations and input the text into the first AI model.

The processor may be further configured to execute the one or moreinstructions to determine a privacy level for the first device knowledgegraph and input the determined privacy level to the first AI model; anda part of data in the first device knowledge graph output from the firstAI model may include data abstracted according to the privacy level.

The processor may be further configured to execute the one or moreinstructions to input a category to the first AI model; and the firstdevice knowledge graph corresponding to the input category may be outputfrom the first AI model.

The first AI model may output the first device knowledge graph throughat least one of entity extraction, entity analysis and abstraction, andrelation extraction.

The processor may be further configured to execute the one or moreinstructions to: control to transmit, to the server, information about auser profile of the user; and control to receive, from the server, theserver knowledge graph related to the user profile.

The processor may be further configured to execute the one or moreinstructions to control to transmit a certain category to the server andto receive, from the server, the server knowledge graph corresponding tothe certain category.

The processor may be further configured to execute the one or moreinstructions to: determine content to be recommended to the user byinputting, to a third AI model, operation information of the device andthe second device knowledge graph; and control request a contentproviding server for the determined content to be recommended.

In accordance with another aspect of the disclosure, a method, performedby a device, of providing content based on a knowledge graph, includes:obtaining context information related to the device; obtaining a firstdevice knowledge graph of a user of the device by inputting the obtainedcontext information to a first artificial intelligence (AI) model fordetermining a relation between entities related to the user of thedevice; requesting, from a server, a server knowledge graph generated bythe server; receiving, from the server, the server knowledge graph;obtaining a second device knowledge graph of the user by inputting theobtained first device knowledge graph and the received server knowledgegraph to a second AI model for extending the first device knowledgegraph; and providing content based on the obtained second deviceknowledge graph.

Each of the first AI model and the second AI model may be an AI modeltrained by using, as an AI algorithm, at least one of a machine learningalgorithm, a neural network algorithm, a genetic algorithm, a deeplearning algorithm, or a classification algorithm.

The server knowledge graph may be generated by the server based on bigdata provided to the server by the device and at least one other device.

The method may further include processing the context information intotext indicating sequential operations, wherein the obtaining the firstdevice knowledge graph may include inputting the text to the first AImodel.

The method may further include determining a privacy level for the firstdevice knowledge graph, wherein the obtaining the first device knowledgegraph may include inputting the privacy level to the first AI model, andwherein part of data in the first device knowledge graph output from thefirst AI model may include data abstracted according to the privacylevel.

The obtaining the first device knowledge graph may include inputting acategory to the first AI model; and the first device knowledge graphcorresponding to the input category may be output from the first AImodel.

The first AI model may output the first device knowledge graph throughat least one of entity extraction, entity analysis and abstraction, andrelation extraction.

The requesting the server knowledge graph from the server may includetransmitting, to the server, information about a user profile of theuser; and the receiving the server knowledge graph may includereceiving, from the server, the server knowledge graph related to theuser profile.

The requesting the server knowledge graph from the server may includetransmitting a certain category to the server; and the receiving theserver knowledge graph may include receiving, from the server, theserver knowledge graph corresponding to the certain category.

In accordance with another aspect of the disclosure, a non-transitorycomputer-readable recording medium has recorded thereon a programexecutable by at least one processor to perform: obtaining contextinformation related to a device; obtaining a first device knowledgegraph of a user of the device by inputting the obtained contextinformation to a first artificial intelligence (AI) model fordetermining a relation between entities related to the user of thedevice; controlling to request, from a server, a server knowledge graphgenerated by the server; controlling to receive, from the server, theserver knowledge graph; obtaining a second device knowledge graph of theuser by inputting the obtained first device knowledge graph and thereceived server knowledge graph to a second AI model for extending thefirst device knowledge graph; and providing content based on theobtained second device knowledge graph.

Each of the first AI model and the second AI model may be an AI modeltrained by using, as an AI algorithm, at least one of a machine learningalgorithm, a neural network algorithm, a genetic algorithm, a deeplearning algorithm, or a classification algorithm.

The server knowledge graph may be generated by the server based on bigdata provided to the server by the device and at least one other device.

The program may be executable by the at least one processor to furtherperform processing the context information into text indicatingsequential operations; and the obtaining the first device knowledgegraph may include inputting the text to the first AI model.

The program may be executable by the at least one processor to furtherperform determining a privacy level for the first device knowledgegraph; the obtaining the first device knowledge graph may includeinputting the privacy level to the first AI model; and part of data inthe first device knowledge graph output from the first AI model mayinclude data abstracted according to the privacy level.

The obtaining the first device knowledge graph may include inputting acategory to the first AI model; and the first device knowledge graphcorresponding to the input category may be output from the first AImodel.

In accordance with another aspect of the disclosure, a device forproviding content based on a knowledge graph includes: a memory storingone or more instructions; and a processor configured to execute the oneor more instructions to: obtain a first device knowledge graph of a userof the device by inputting, to a first artificial intelligence (AI)model for determining a relation between entities related to the user ofthe device, context information related to the device; control toreceive, from a server, a server knowledge graph; obtain a second deviceknowledge graph of the user by inputting the obtained first deviceknowledge graph and the received server knowledge graph to a second AImodel for extending the first device knowledge graph; and obtain contentbased on the obtained second device knowledge graph.

Each of the first AI model and the second AI model may be an AI modeltrained by using, as an AI algorithm, at least one of a machine learningalgorithm, a neural network algorithm, a genetic algorithm, a deeplearning algorithm, or a classification algorithm.

The server knowledge graph may be generated by the server based on bigdata provided to the server by the device and at least one other device.

The processor may be further configured to execute the one or moreinstructions to process the context information into text indicatingsequential operations and input the text into the first AI model.

The processor may be further configured to execute the one or moreinstructions to determine a privacy level for the first device knowledgegraph and input the determined privacy level to the first AI model; anda part of data in the first device knowledge graph output from the firstAI model may include data abstracted according to the privacy level.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the present disclosure will be more apparent from thefollowing description taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a diagram of a system for providing content based on aknowledge graph about a user, according to an embodiment;

FIG. 2 is a flowchart of a method, performed by a device, of providingcontent by using a knowledge graph based on an artificial intelligence(AI) model, according to an embodiment;

FIG. 3 is a flowchart of a method, performed by a device, of generatinga first device knowledge graph, according to an embodiment;

FIG. 4 is a flowchart of a method, performed by a device, of setting acategory and a privacy level to receive customized content, according toan embodiment;

FIG. 5 is a flowchart of a method, performed by a device, of extending afirst device knowledge graph by using a server knowledge graphcorresponding to a specific category, according to an embodiment;

FIG. 6 is a flowchart of a method, performed by a device, of determiningcontent to be provided to a user by using operation information of thedevice and a second device knowledge graph, according to an embodiment;

FIG. 7 is a diagram of a method, performed by a system, of providingcustomized content to a user, according to an embodiment;

FIG. 8 is a diagram illustrating an example in which a device generatesa first device knowledge graph by using text indicating context relatedto the device, according to an embodiment;

FIG. 9 is a diagram illustrating an example in which a device selects acategory to receive customized content, according to an embodiment;

FIG. 10 is a diagram illustrating an example in which a device selects aprivacy level for a category, according to an embodiment;

FIG. 11 is a diagram illustrating a first device knowledge graph and asecond device knowledge graph, according to an embodiment;

FIG. 12 is a diagram illustrating an example in which a device generatesa first device knowledge graph by using a first AI model, according toan embodiment;

FIG. 13 is a diagram illustrating an example in which a device generatesa first device knowledge graph and a second device knowledge graph byusing a first AI model and a second AI model, according to anembodiment;

FIG. 14 is a diagram illustrating an example in which a devicedetermines recommended content by using a third AI model, according toan embodiment;

FIG. 15 is a diagram illustrating an example in which a device generatesa second device knowledge graph by using a fourth AI model, according toan embodiment;

FIG. 16 is a diagram illustrating an example in which a devicedetermines recommended content by using a fifth AI model, according toan embodiment;

FIG. 17 is a block diagram of a device according to an embodiment;

FIG. 18 is a block diagram of a device according to an embodiment; and

FIG. 19 is a block diagram of a server according to an embodiment.

DETAILED DESCRIPTION

Hereinafter, embodiments of the disclosure will be described in detailin order to fully convey the scope of the disclosure and enable one ofordinary skill in the art to easily embody and practice the disclosure.Embodiments of the disclosure may be implemented in various forms andthe disclosure is not limited to specific embodiments described indetail below. Also, like reference numerals in the drawings denote likeelements.

Throughout the disclosure, expressions such as “at least one of a, b orc” and “at least one of a, b and c” indicates only a, only b, only c,both a and b, both a and c, both b and c, all of a, b, and c, orvariations thereof.

Throughout the specification, it will be understood that when an elementis referred to as being “connected” to another element, the element maybe “directly connected” to the other element or “electrically connected”to the other element with intervening elements therebetween. It will befurther understood that when a part “includes” or “comprises” anelement, unless otherwise defined, the part may further include otherelements, not excluding the other elements.

In the specification, a device knowledge graph may be a knowledge graphgenerated in a user's device. The device knowledge graph may begenerated by reflecting the user's privacy information collected in theuser's device.

Additionally, a server knowledge graph may be a knowledge graphgenerated in a server. The server knowledge graph may be generated bythe server based on big data collected from various devices.

Also, an artificial intelligence (AI) model may be an AI model trainedby using at least one of a machine learning algorithm, a neural networkalgorithm, a genetic algorithm, a deep learning algorithm, or aclassification algorithm as an AI algorithm.

Furthermore, content may be digital information provided through awired/wireless communication network. Examples of the content mayinclude, but are not limited to, advertising content, moving imagecontent (e.g., TV programs, videos on-command (VODs), user-createdcontent (UCC), music videos, or YOUTUBE videos), still image content(e.g., photographs or pictures), text content (e.g., electronic booksincluding poetry and novels, letters, or work files), music content(e.g., music, performances, or radio broadcasts), web pages, andapplication execution information.

Embodiments will now be described more fully with reference to theaccompanying drawings.

FIG. 1 is a diagram of a system for providing content based on aknowledge graph about a user according to an embodiment.

Referring to FIG. 1, a system for providing content based on a knowledgegraph about a user may include a device 1000, a server 2000, and acontent providing server 3000.

The device 1000 may collect context information about the device 1000and may generate a device knowledge graph by using the collected contextinformation and at least one AI model. Also, the device 1000 may receivea server knowledge graph generated by the server 2000 from the server2000 and may extend or expand the device knowledge graph by using thereceived server knowledge graph and at least one AI model. The deviceknowledge graph generated and extended by the device 1000 may includeinformation related to the user's privacy, and the device knowledgegraph including privacy information may be used and managed in thedevice 1000. Also, the device 1000 may determine content to berecommended to the user by using the extended device knowledge graph andmay receive the determined recommended content from the contentproviding server 3000.

The server 2000 may generate the server knowledge graph based on bigdata received from various devices 1000. The big data used to generatethe server knowledge graph may be data excluding the information relatedto the user's privacy. For example, the big data may include informationabstracted from the privacy information of the user.

The content providing server 3000 may be a server that manages andprovides content to the device 1000. The content providing server 3000may be a server that provides, for example, a movie, a game, anadvertisement, etc.

Examples of the device 1000 may include, but are not limited to, asmartphone, a tablet personal computer (PC), a PC, a smart television(TV), a mobile phone, a personal digital assistant (PDA), a laptop, amedia player, a micro-server, a global positioning system (GPS) device,an electronic book terminal, a digital broadcast terminal, a navigationsystem, a kiosk, an MP3 player, a digital camera, a home appliance, aportable media device, and any of other mobile or non-mobile computingdevices. Also, examples of the device 1000 may include a wearable devicesuch as a watch, glasses, a hair band, or a ring having a communicationfunction and a data processing function. It is understood, however, thatthe disclosure is not limited thereto, and the device 1000 may includeany type of device that may transmit/receive data through a networkto/from the server 2000 and the content providing server 3000.

Examples of the network may include a local area network (LAN), a widearea network (WAN), a value-added network (VAN), a mobile radiocommunication network, a satellite communication network, and acombination thereof. Further, the network is a data communicationnetwork for smooth communication between network components in a broadsense, and examples of the network may include a wired Internet, awireless Internet, and a mobile wireless communication network.

Examples of wireless communication may include, but are not limited to,Wi-Fi, Bluetooth, Bluetooth low energy, Zigbee, Wi-Fi Direct (WFD),ultra-wideband (UWB), infrared data association (IrDA), and near-fieldcommunication (NFC).

FIG. 2 is a flowchart of a method, performed by a device 1000, ofproviding content by using a knowledge graph based on an AI modelaccording to an embodiment.

Referring to FIG. 2, in operation S200, the device 1000 may obtaincontext information related to the device 1000. The device 1000 maymonitor an operation of an application in the device 1000 and maycollect information related to the device 1000 from a sensor in thedevice 1000 and another device connected to the device 1000.

The context information may include at least one of, but not limited to,ambient environment information of the device 1000, state information ofthe device 1000, state information of a user who uses the device 1000, adevice usage history information of the user, or schedule information ofthe user. The ambient environment information of the device 1000 refersto environment information within a certain radius from the device 1000and may include, but is not limited to, at least one of weatherinformation, temperature information, humidity information, illuminanceinformation, noise information, and sound information. The stateinformation of the device 1000 may include, but is not limited to, atleast one of information about a mode of the device 1000 (e.g., a soundmode, a vibration mode, a silent mode, a power saving mode, a cutoffmode, a multi-window mode, or an automatic rotation mode), positioninformation of the device 1000, time information, activation informationof a communication module (e.g., Wi-Fi ON, Bluetooth OFF, GPS ON, or NFCON), network connection state information of the device 1000, andinformation about an application executed by the device 1000 (e.g.,application identification information, an application type, anapplication usage time, or an application usage cycle). The stateinformation of the user refers to information about the user's movementor life pattern and may include, but is not limited to, at least one ofinformation about the user's walking state, exercising state, drivingstate, sleep state, and mood state. The device usage history informationof the user refers to information about events where the user uses thedevice 1000 and may include, but is not limited to, information about atleast one of execution of applications, functions executed by theapplications, the user's phone conversations, and the user's textmessages.

In operation S210, the device 1000 may obtain a first device knowledgegraph by inputting the context information to a first AI model. Forexample, the device 1000 may process the context information and mayobtain a first device knowledge graph by inputting the processed contextinformation to the first AI model.

The first device knowledge graph may be a knowledge graph generatedbased on context related to the user and/or the device 1000 and may begenerated by reflecting information related to the user's privacy. Thefirst AI model may be an AI model that may generate and update aknowledge graph based on the context of the user and/or the device 1000.The AI model may be trained by using at least one of a machine learningalgorithm, a neural network algorithm, a genetic algorithm, a deeplearning algorithm, or a classification algorithm as an AI algorithm.The first AI model may function to extract entities in the contextinformation and infer a relation between the extracted entities.

The device 1000 may generate the first device knowledge graph for eachof certain categories. The device 1000 may generate the first deviceknowledge graph according to a privacy level for protecting the user'spersonal information. The privacy level may indicate a degree to whichthe user's personal information is protected, and a degree to which datarelated to the user's privacy in data in the first device knowledgegraph is abstracted may be determined according to the privacy level.

In operation S220, the device 1000 may request a server knowledge graphfrom the server 2000. For example, the device 1000 may transmit at leastone of information related to the user's profile and an identificationvalue of a category selected by the user to the server 2000, and mayrequest the server knowledge graph from the server 2000.

The server knowledge graph, which is a knowledge graph generated by theserver 2000, may be a knowledge graph generated based on big datareceived from various users and various devices. The big data used togenerate the server knowledge graph may include context informationrelated to various circumstances, and may be data excluding informationrelated to individual privacy. Also, the server knowledge graph may begenerated by a certain AI model using the big data as an input value,and, for example, may be generated for each category and profilecharacteristics of the user.

In operation S230, the device 1000 may receive the server knowledgegraph from the server 2000. The device 1000 may receive the serverknowledge graph related to the user's profile. Furthermore, the device1000 may receive the server knowledge graph related to the categoryselected by the user.

In operation S240, the device 1000 may obtain a second device knowledgegraph by inputting the first device knowledge graph and the serverknowledge graph to a second AI model. The second device knowledge graphmay be a knowledge graph extended from the first device knowledge graph.The second AI model may be an AI model for extending and updating thefirst device knowledge graph. The second AI model may be trained byusing at least one of a machine learning algorithm, a neural networkalgorithm, a genetic algorithm, a deep learning algorithm, or aclassification algorithm as an AI algorithm. The second AI model mayfunction to extend the first device knowledge graph by analyzing andintegrating the first device knowledge graph and the server knowledgegraph.

In operation S250, the device 1000 may provide content based on thesecond device knowledge graph. The device 1000 may identify a currentcontext of the device 1000 and may determine content suitable for thecurrent context as recommended content by using the second deviceknowledge graph. Also, the device 1000 may request the determinedrecommended content from the content providing server 3000 and mayreceive the requested recommended content from the content providingserver 3000.

Content may be digital information provided through a wired/wirelesscommunication network. Examples of the content may include, but are notlimited to, advertising content, moving image content (e.g., TVprograms, VODs, UCC, music videos, or YOUTUBE videos), still imagecontent (e.g., photographs or pictures), text content (e.g., electronicbooks including poetry and novels, letters, or work files), musiccontent (e.g., music, performances, or radio broadcasts), web pages,gaming content, application execution information, etc.

FIG. 3 is a flowchart of a method, performed by a device 1000, ofgenerating a first device knowledge graph according to an embodiment.

Referring to FIG. 3, in operation S300, the device 1000 may obtaincontext information related to the device 1000. The context informationmay include at least one of, but not limited to, ambient environmentinformation of the device 1000, state information of the device 1000,state information of a user who uses the device 1000, a device usagehistory information of the user, or schedule information of the user. Bymonitoring an operation of an application installed in the device 1000,the device 1000 may collect, for example, at least one of an operationexecuted by the application, data input to the application, and dataoutput through the application. Also, by obtaining sensing data sensedby sensors in the device 1000, the device 1000 may obtain informationabout an ambient environment of the device 1000 and information about astate of the device 1000.

In operation S310, the device 1000 may process the context informationinto text indicating sequential operations. The device 1000 maygenerate, based on the context information, sentences indicating contextrelated to the device 1000 and/or the user. For example, the device 1000may generate text such as “user has downloaded a travel application,”“user has searched Okinawa,” and “user purchased a camera at 9 a.m. onSaturday.”

In operation S320, the device 1000 may set a privacy level of a deviceknowledge graph. In order to abstract data related to the user's privacyin data in the device knowledge graph, the device 1000 may set theprivacy level of the device knowledge graph. For example, the device1000 may display a graphical user interface (GUI) for setting theprivacy level of the device knowledge graph and may set the privacylevel of the device knowledge graph based on a user input through theGUI.

In operation S330, the device 1000 may apply the text in operation S310and the privacy level set in operation S320 to a first AI model. Thedevice 1000 may abstract data related to individual privacy by inputtingthe text and the privacy level to the first AI model, and first userknowledge data based on the abstracted data may be output from the firstAI model. Also, according to the privacy level, a degree to which thedata related to the user's privacy in data in a first device knowledgegraph is abstracted may be determined according to the privacy level.

For example, when text “user has purchased a Big Mac burger fromMcDonald's” and a certain privacy level value “1” are input to the firstAI model, the first device knowledge graph based on abstracted data“user has purchased a hamburger from McDonald's” may be output. Also,for example, when text “user has purchased a Big Mac burger fromMcDonald's” and a certain privacy level value ‘2’ are input to the firstAI model, the first device knowledge graph based on abstracted data“user has purchased a hamburger from a hamburger store” may be output.

FIG. 4 is a flowchart of a method, performed by a device 1000, ofsetting a category and a privacy level to receive customized contentaccording to an embodiment.

Referring to FIG. 4, in operation S400, the device 1000 may select atleast one category for receiving user customized content in a categorylist. The device 1000 may display a certain category list on a screen ofthe device 1000 and may receive a user input that selects a specificcategory in the certain category list. Categories may be dividedaccording to at least one of a type of content, a field of the content,or a size of the content. For example, categories may be divided into,but not limited to, a movie, a sport, a game, and an advertisement.

In operation S410, the device 1000 may set a privacy level for eachselected category. The device 1000 may display a GUI for inputting theprivacy level on the screen of the device 1000 and may set the privacylevel for each category based on a user input through the GUI.Accordingly, the privacy level may be differently set for each category,and the device 1000 may generate a device knowledge graph for eachcategory according to the differently set privacy level.

FIG. 5 is a flowchart of a method, performed by a device 1000, ofextending a first device knowledge graph by using a server knowledgegraph corresponding to a specific category according to an embodiment.

Referring to FIG. 5, in operation S500, the device 1000 may select acategory of a knowledge graph, and in operation S510, the device 1000may request a server knowledge graph corresponding to the selectedcategory from the server 2000. The server 2000 may generate and storethe server knowledge graph for each of certain categories by using bigdata received from various devices. In this case, by inputting the bigdata and a category value to a certain AI model, the server 2000 maygenerate the server knowledge graph for each category.

In operation S520, the device 1000 may receive the server knowledgegraph from the server 2000. For example, the device 1000 may receive theserver knowledge graph corresponding to the selected category from theserver 2000.

In operation S530, the device 1000 may apply a first device knowledgegraph, the server knowledge graph, and a privacy level value to a secondAI model. The device 1000 may extract the first device knowledge graphcorresponding to the certain category from a memory of the device 1000and may input the extracted first device knowledge graph and the serverknowledge graph corresponding to the certain category to the second AImodel. Also, the device 1000 may input the privacy level value set forthe first device knowledge graph corresponding to the certain categoryto the second AI model. Accordingly, the device 1000 may obtain a seconddevice knowledge graph extended from the first device knowledge graphfrom the second AI model.

FIG. 6 is a flowchart of a method, performed by a device 1000, ofdetermining content to be provided to a user by using operationinformation of the device 1000 and a second device knowledge graphaccording to an embodiment.

Referring to FIG. 6, in operation S600, the device 1000 may obtain asecond device knowledge graph. The device 1000 may obtain the seconddevice knowledge graph extended from a first device knowledge graph byusing a server knowledge graph.

In operation S610, the device 1000 may obtain operation information ofthe device 1000 in real time. The device 1000 may obtain context relatedto the device 1000, such as at least one of an application that is beingexecuted in the device 1000, a function of the application that is beingexecuted, an ambient environment of the device 1000, and scheduleinformation stored in the device 1000, as the operation information ofthe device 1000 in real time.

In operation S620, the device 1000 may determine recommended content tobe provided to a user by applying the second device knowledge graph andthe operation information of the device 1000 to a third AI model. Thethird AI model may be an AI model for determining the recommendedcontent to be recommended to the user based on the operation informationof the device 1000 and/or the user and the second device knowledgegraph. The third AI model may be trained by using at least one of amachine learning algorithm, a neural network algorithm, a geneticalgorithm, a deep learning algorithm, or a classification algorithm asan AI algorithm. The third AI model may function to recommend content ofa field in which the user is interested, according to a context of thedevice 1000 and the second device knowledge graph based on real timeoperation information of the device 1000.

In operation S630, the device 1000 may provide the determinedrecommended content to the user. For example, the device 1000 mayidentify the content providing server 3000 that provides the determinedrecommended content and may request the recommended content from thecontent providing server 3000. Also, the device 1000 may receive therecommended content from the content providing server 3000. Accordingly,the device 1000 may provide the recommended content suitable for theuser to the user, without providing information related to the user'sprivacy to the content providing server 3000. Also, examples of therecommended content may include, but are not limited to, advertisingcontent, moving image content (e.g., TV programs, VODs, UCC, musicvideos, or YOUTUBE videos), still image content (e.g., photographs orpictures), text content (e.g., electronic books including poetry andnovels, letters, or work files), music content (e.g., music,performances, or radio broadcasts), web pages, gaming content, andapplication execution information.

FIG. 7 is a diagram of a method, performed by a system, of providingcustomized content to a user according to an embodiment.

Referring to FIG. 7, in operation S700, the device 1000 may obtaincontext information. The device 1000 may collect the context informationrelated to the device 1000 and/or a user and may process the collectedcontext information into certain text.

In operation S705, the device 1000 may obtain a first device knowledgegraph by inputting the context information to a first AI model. Forexample, the device 1000 may input the text processed from the contextinformation to the first AI model. Also, the device 1000 may input aprivacy level and a category value to the first AI model. The device1000 may obtain the first device knowledge graph from the first AImodel.

In operation S710, the server 2000 may generate a server knowledgegraph. The server 2000 may collect various kinds of big data fromvarious devices and may generate the server knowledge graph by using thecollected big data. The big data collected by the server 2000 may bedata abstracted from individual privacy information.

In operation S715, the device 1000 may request the server knowledgegraph from the server 2000. For example, the device 1000 may transmitinformation about the user's profile and/or the category value to theserver 2000 and may request the server knowledge graph from the server2000. In this case, information about the user's privacy in theinformation about the user's profile may be abstracted information.

In operation S720, the device 1000 may receive the server knowledgegraph from the server 2000. The server 2000 may extract the serverknowledge graph to be provided to the device 1000 from a database (DB)in consideration of the user's profile and/or a category and may providethe extracted server knowledge graph to the device 1000. In this case,the server knowledge graph may be generated according to the user'sprofile and/or the category and may be stored in the DB of the server2000.

In operation S725, the device 1000 may obtain a second device knowledgegraph by inputting the first device knowledge graph and the serverknowledge graph to a second AI model. The second AI model may output thesecond device knowledge graph by extending the first device knowledgegraph using the server knowledge graph.

In operation S730, the device 1000 may remove privacy information in thesecond device knowledge graph. The device 1000 may abstract data relatedto the user's privacy in data in the second device knowledge graph.

In operation S735, the device 1000 may provide a device knowledge graphfrom which the privacy information is removed to the server 2000.

In operation S740, the server 2000 may update the server knowledge graphby using the device knowledge graph from which the privacy informationis removed. By inputting the server knowledge graph generated inoperation S710 and the device knowledge graph received in operation S735to a certain AI model, the server 2000 may update the server knowledgegraph. The updated server knowledge graph may later be used by thedevice 1000 to generate the second device knowledge graph.

In operation S745, the device 1000 may determine recommended content tobe provided to the user by applying the second device knowledge graph toa third AI model. The device 1000 may receive information about therecommended content from the third AI model by inputting operationinformation of the device 1000 and the second device knowledge graph tothe third AI model. The information about the recommended content mayinclude at least one of, but not limited to, an identification value ofthe recommended content, a type of the recommended content, or a fieldof the recommended content.

In operation S750, the device 1000 may request the recommended contentfrom the content providing server 3000, and in operation S755, thedevice 1000 may receive the recommended content from the contentproviding server 3000.

FIG. 8 is a diagram illustrating an example in which a device 1000generates a first device knowledge graph by using text indicatingcontext related to the device according to an embodiment.

Referring to FIG. 8, the device 1000 may obtain context informationabout a device usage record and personal information stored in or inputto the device 1000. Also, the device 1000 may generate text such as“user has downloaded a travel application,” “user has searched Okinawa,”and “user has purchased a camera.”

The device 1000 may input the generated text to a first AI model, andthe first AI model may input a first device knowledge graph definingentities and a relation between the entities. For example, the entitiesof the first device knowledge graph may include “I,” “Okinawa,”“camera,” and “travel application.” In this case, a relation between theentity “I” and the entity “Okinawa” may be “search.” Furthermore, inthis example, a relation between the entity “I” and the entity “camera”may be “purchase.” Also, a relation between the entity “I” and theentity “travel application” may be “download.”

FIG. 9 is a diagram illustrating an example in which a device 1000selects a category to receive customized content according to anembodiment.

Referring to FIG. 9, in order for a user to receive user customizedcontent for only a specific category, the device 1000 may display acategory list for selecting the specific category on a screen of thedevice 1000. For example, the device 1000 may display a guide paragraphfor allowing the user to select a category and may displayidentification values of categories such as “movie,” “sport,” “game,”and “advertisement,” thereby making it possible for the user to select aspecific category.

FIG. 10 is a diagram illustrating an example in which a device 1000selects a privacy level for a category according to an embodiment.

Referring to FIG. 10, the device 1000 may display a GUI for allowing auser to set a privacy level for each category and may set a privacylevel for each category based on a user input through the GUI. Forexample, the device 1000 may set a privacy level of a category “movie”to “2,” may set a privacy level of a category “sport” to “1,” may set aprivacy level of a category “game” to “3,” and may set a privacy levelof a category “advertisement” to “4,” based on a user input through theGUI. Accordingly, a first device knowledge graph of the category“movie,” a first device knowledge graph of the category “sport,” a firstdevice knowledge graph of the category “game,” and a first deviceknowledge graph of the category “advertisement” may be generated atdifferent privacy levels.

FIG. 11 is a diagram illustrating a first device knowledge graph and asecond device knowledge graph according to an embodiment.

Referring to FIG. 11, entities of a first device knowledge graph 110 mayinclude “I,” “Okinawa,” “camera,” and “travel application.” Also, forexample, a relation between the entity “I” and the entity “Okinawa” maybe “search,” a relation between the entity “I” and the entity “camera”may be “purchase,” and a relation between the entity “I” and the entity“travel application” may be “download.”

The device 1000 may generate a second device knowledge graph 120 byinputting the first device knowledge graph 110 and a server knowledgegraph received from the server 2000 to a first AI model. The seconddevice knowledge graph 120 may be a knowledge graph extended from thefirst device knowledge graph 110. The entities in the first deviceknowledge graph 110 and entities in the server knowledge graph may bemapped by certain criteria, and the entities in the server knowledgegraph may be integrated with the entities in the first device knowledgegraph 110 according to certain criteria. For example, the second deviceknowledge graph 120 may include entities “restaurant” and “aquarium”extended from the entity “Okinawa.” Also, for example, a relationbetween the entity “Okinawa” and the entity “restaurant” may bedetermined to be “food,” and a relation between the entity “Okinawa” andthe entity “aquarium” may be determined to be “tourism.”

FIG. 12 is a diagram illustrating an example in which a device 1000generates a first device knowledge graph by using a first AI modelaccording to an embodiment.

Referring to FIG. 12, the device 1000 may generate structured data bycollecting and preprocessing context information and may generate afirst device knowledge graph by using the structured data. Thestructured data may be text indicating, for example, sequentialoperations and may be sentences indicating context related to the device1000 and/or a user.

The device 1000 may input the structured data to a first AI model, andthe first AI model may generate a first device graph through entityextraction, entity resolution and disambiguation (entity analysis andabstraction), and relation extraction by using the structured data as aninput value. The first AI model may be implemented by using, forexample, an ontology learning module and may be implemented by using anetwork including a plurality of layers.

FIG. 13 is a diagram illustrating an example in which a device 1000generates a first device knowledge graph and a second device knowledgegraph by using a first AI model 130 and a second AI model 132 accordingto an embodiment.

Referring to FIG. 13, a first AI model 130 may receive contextinformation, a privacy level, and a category and may output a firstdevice knowledge graph. The context information may be processed intotext indicating sequential operations and may be input to the first AImodel 130. Also, the first AI model 130 may output the first deviceknowledge graph corresponding to the input category. Furthermore, datain the first device knowledge graph may be extracted to protect a user'sprivacy based on the input privacy level.

A second AI model 132 may receive the first device knowledge graph and aserver knowledge graph and may output a second device knowledge graph.The server knowledge graph may be generated by a server based on bigdata from various devices. Also, the second AI model 132 may output thesecond device knowledge graph by extending the first device knowledgegraph by using the server knowledge graph.

FIG. 14 is a diagram illustrating an example in which a device 1000determines recommended content by using a third AI model 140 accordingto an embodiment.

Referring to FIG. 14, a third AI model 140 may receive operationinformation of the device 1000, a second device knowledge graph, andprivacy setting information and may determine recommended content. Theoperation information of the device 1000 may be collected in real timeby the device 1000 and may be input to the third AI model 140. In thiscase, the operation information of the device 1000 may be preprocessedin a preset format. The third AI model 140 may determine recommendedcontent to be recommended to a user based on the privacy settinginformation. The third AI model 140 may output, but is not limited to,an identification value of the recommended content. The third AI model140 may output at least one of a type of the recommended content, acategory, or link information. Also, the third AI model 140 may receiveinformation about the category set by the user and may output therecommended content related to the category set by the user.

FIG. 15 is a diagram illustrating an example in which a device 1000generates a second device knowledge graph by using a fourth AI model 150according to an embodiment.

Referring to FIG. 15, a fourth AI model 150 may receive a first deviceknowledge graph, context information, a privacy level, a category, and aserver knowledge graph and may output a second device knowledge graph.The fourth AI model 150 may be an AI model for extending and updatingthe first device knowledge graph. The fourth AI model 150 may be trainedby using at least one of a machine learning algorithm, a neural networkalgorithm, a genetic algorithm, a deep learning algorithm, or aclassification algorithm as an AI algorithm. The fourth AI model 150 mayfunction to extend the first device knowledge graph by analyzing andintegrating the first device knowledge graph and the server knowledgegraph.

FIG. 16 is a diagram illustrating an example in which a device 1000determines recommended content by using a fifth AI model 160 accordingto an embodiment.

Referring to FIG. 16, a fifth AI model 160 may receive a first deviceknowledge graph, context information, a privacy level, a category, and aserver knowledge graph and may output recommended content. In this case,the fifth AI model 160 may be an AI model for extending the first deviceknowledge graph by using the server knowledge graph and determining therecommended content. The fifth AI model 160 may be trained by using atleast one of a machine learning algorithm, a neural network algorithm, agenetic algorithm, a deep learning algorithm, or a classificationalgorithm as an AI algorithm.

FIGS. 17 and 18 are block diagrams of a device 1000 according to one ormore embodiments.

As shown in FIG. 17, the device 1000 according to an embodiment mayinclude a user inputter 1100, an outputter 1200, a controller 13000, anda communicator 1500. It is understood, however, that the device 1000 mayinclude more or less elements than the elements illustrated in FIG. 17.

For example, as shown in FIG. 18, the device 1000 according to anembodiment may further include a sensing unit 1400 (e.g., sensor), anaudio/video (NV) inputter 1600, and a memory 1700 in addition to theuser inputter 1100, the outputter 1200, the controller 1300, and thecommunicator 1500.

The user inputter 1100 is a unit through which a user inputs data forcontrolling the device 1000. Examples of the user inputter 1100 mayinclude, but are not limited to, at least one of a keypad, a domeswitch, a touchpad (e.g., a contact-type capacitance method, apressure-type resistance film method, an infrared sensing method, asurface ultrasound transmission method, an integral tension measuringmethod, or a piezoelectric effect method), a proximity sensor, a gesturesensor, a jog wheel, and a jug switch.

The user inputter 1100 may receive a user input for providingrecommended content based on a device knowledge graph.

The outputter 1200 may output an audio signal, a video signal, or avibration signal and may include a display 1210, a sound outputter 1220,and a vibration motor 1230.

The display 1210 displays and outputs information processed by thedevice 1000. For example, the display 1210 may display a user interfacefor providing the recommended content based on the device knowledgegraph.

When the display 1210 and a touchpad have a layer structure to form atouchscreen, the display 1210 may be used as an input device as well asan output device.

The sound outputter 1220 outputs audio data received from thecommunicator 1500 or stored in the memory 1700. Also, the soundoutputter 1220 outputs a sound signal (e.g., a call signal receivingsound, a message receiving sound, or a notification sound) related to afunction performed by the device 1000. The sound outputter 1220 mayinclude a speaker or a buzzer.

The vibration motor 1230 may output a vibration signal. For example, thevibration motor 1230 may output a vibration signal corresponding to anoutput of audio data or video data (e.g., a call signal receiving sound,a message receiving sound, or a notification sound). Also, the vibrationmotor 1230 may output a vibration signal when a touch is input to atouchscreen.

The controller 1300 (e.g., at least one processor) generally controls anoverall operation of the device 1000. For example, the controller 1300may control the user inputter 1100, the outputter 1200, the sensing unit1400, the communicator 1500, and the AN inputter 1600 by executingprograms or instructions stored in the memory 1700. The controller 1300may control an operation of the device 1000 by controlling the userinputter 1100, the outputter 1200, the sensing unit 1400, thecommunicator 1500, and the AN inputter 1600.

In detail, the controller 1300 may obtain context information related tothe device 1000. The controller 1300 may monitor an operation of anapplication in the device 1000 and may collect information related tothe device 1000 from a sensor in the device 1000 and another deviceconnected to the device 1000.

The controller 1300 may obtain a first device knowledge graph byinputting the context information to a first AI model. The controller1300 may obtain the first device knowledge graph by processing thecontext information and inputting the processed context information tothe first AI model. The controller 1300 may generate the first deviceknowledge graph for each of certain categories. The controller 1300 maygenerate the first device knowledge graph according to a privacy levelfor protecting the user's personal information.

The controller 1300 may request a server knowledge graph from the server2000. The controller 1300 may request the server knowledge graph fromthe server 2000 by transmitting information related to the user'sprofile and an identification value of a category selected by the userto the server 2000.

The controller 1300 may receive the server knowledge graph from theserver 2000. The controller 1300 may receive the server knowledge graphrelated to the user's profile. Furthermore, the controller 1300 mayreceive the server knowledge graph related to the category selected bythe user.

The controller 1300 may obtain a second device knowledge graph byinputting the first device knowledge graph and the server knowledgegraph to a second AI model. The second device knowledge graph may be aknowledge graph extended from the first device knowledge graph.

The controller 1300 may provide content based on the second deviceknowledge graph. The controller 1300 may identify a current context ofthe device 1000 and may determine content suitable for the currentcontext as recommended content by using the second device knowledgegraph. Also, the controller 1300 may request the determined recommendedcontent from the content providing server 3000 and may receive therequested recommended content from the content providing server 3000.

The controller 1300 may process the context information into textindicating sequential operations. For example, the controller 1300 maygenerate sentences indicating context related to the device 1000 and/orthe user based on the context information.

The controller 1300 may set a privacy level of a device knowledge graph.The controller 1300 may set the privacy level of the device knowledgegraph, in order to abstract data related to the user's privacy amongdata in the device knowledge graph.

The controller 1300 may apply the text and the privacy level to thefirst AI model. The controller 1300 may abstract data related toindividual privacy by inputting the text and the privacy level to thefirst AI model, and first user knowledge data based on the abstracteddata may be output from the first AI model.

The controller 1300 may select at least one category for receiving usercustomized content in a category list. The controller 1300 may display acertain category list on a screen of the device 1000 and may receive auser input that selects a specific category in the certain categorylist.

The controller 1300 may set a privacy level for each selected category.The controller 1300 may display a GUI for inputting the privacy level onthe screen of the device 1000 and may set the privacy level for eachcategory based on a user input through the GUI. Accordingly, the privacylevel may be differently set for each category, and the controller 1300may generate the device knowledge graph for each category according tothe differently set privacy level.

The controller 1300 may obtain operation information of the device 1000in real time. The controller 1300 may obtain context related to thedevice 1000, such as an application that is being executed in the device1000, a function of the application that is being executed, an ambientenvironment of the device 1000, and schedule information stored in thedevice 1000, as the operation information of the device 1000 in realtime.

By applying the second device knowledge graph and the operationinformation of the device 1000 to a third AI model, the controller 1300may determine recommended content to be provided to the user.

The controller 1300 may provide the determined recommended content tothe user. The controller 1300 may identify the content providing server3000 that provides the determined recommended content and may requestthe recommended content from the identified content providing server3000. Also, the controller 1300 may receive the recommended content fromthe content providing server 3000. Accordingly, the controller 1300 mayprovide the recommended content suitable for the user to the user,without providing information related to the user's privacy to thecontent providing server 3000.

The sensing unit 1400 may detect a state of the device 1000 or a statearound the device 1000, and may transmit detected information to thecontroller 1300.

The sensing unit 1400 may include at least one of, but not limited to, aterrestrial magnetism sensor 1410, an acceleration sensor 1420, atemperature/humidity sensor 1430, an infrared sensor 1440, a gyroscopesensor 1450, a position sensor (e.g., a global positioning system (GPS))1460, a barometric pressure sensor 1470, a proximity sensor 1480, or anRGB sensor (e.g., an illuminance sensor) 1490.

The communicator 1500 may include one or more elements (e.g.,interfaces, circuitry, ports, etc.) through which the device 1000communicates with the server 2000 and the content providing server 3000.For example, the communicator 1500 may include a short-rangecommunicator 1510, a mobile communicator 1520, and a broadcast receiver1530.

Examples of the short-range communicator 1510 may include, but are notlimited to, a Bluetooth communicator, a Bluetooth low energy (BLE)communicator, a near-field communicator, a WLAN (Wi-Fi) communicator, aZigbee communicator, an infrared data association (IrDA) communicator, aWi-Fi Direct (WFD) communicator, an ultra-wideband (UWB) communicator,and an Ant+ communicator.

The mobile communicator 1520 transmits/receives a wireless signalto/from at least one of a base station, an external terminal, or aserver via a mobile communication network. Examples of the wirelesssignal may include a voice call signal, a video call signal, and any ofvarious pieces of data according to text/multimedia messagetransmission/reception.

The broadcast receiver 1530 receives a broadcast signal and/orbroadcast-related information from the outside through a broadcastchannel. Examples of the broadcast channel may include a satellitechannel and a terrestrial channel. According to an embodiment, thedevice 1000 may not include the broadcast receiver 1530.

Also, the communicator 1500 may transmit/receive information used toprovide the recommended content based on the device knowledge graphto/from the server 2000 and the content providing server 3000.

The AN inputter 1600 for inputting an audio signal or a video signal mayinclude a camera 1610 and a microphone 1620. The camera 1610 may obtainimage frames such as a still image or a moving image by using an imagesensor in a video call mode or an imaging mode. An image captured by theimage sensor may be processed by the controller 1300 or an additionalimage processor.

The image frames processed by the camera 16140 may be stored in thememory 1700 or may be transmitted to the outside through thecommunicator 1500. Two or more cameras 1610 may be provided according toa configuration of a terminal.

The microphone 1620 receives an external sound signal and processes theexternal sound signal into electrical voice data. For example, themicrophone 1620 may receive a sound signal from an external device or aspeaker. The microphone 1620 may use any of various noise removingalgorithms to remove noise occurring when (e.g., based on) receiving theexternal sound signal.

The memory 1700 may store a program or instructions for processing andcontrolling the controller 1300 and may store data input to the device1000 or output from the device 1000.

The memory 1700 may include at least one type of storage medium fromamong a flash memory type, a hard disk type, a multimedia card microtype, a card-type memory (e.g., a secure digital (SD) or XD memory), arandom-access memory (RAM), a static RAM (SRAM), a read-only memory(ROM), an electrically erasable programmable ROM (EEPROM), aprogrammable FROM (PROM), a magnetic memory, a magnetic disk, or anoptical disk.

The memory 1700 may store the first device knowledge graph and thesecond device knowledge graph. The memory 1700 may build a database ofthe first device knowledge graph for each category and may store thefirst device knowledge graph. The memory 1700 may build a database ofthe first device knowledge graph for each privacy level and may storethe first device knowledge graph.

Also, the memory 1700 may build a database of the second deviceknowledge graph for each category and may store the second deviceknowledge graph. The memory 1700 may build a database of the seconddevice knowledge graph for each privacy level and may store the seconddevice knowledge graph.

Programs stored in the memory 1700 may be classified into a plurality ofmodules, for example, into a user interface (UI) module 1710, atouchscreen module 1720, and a notification module 1730, according tofunctions of the memory 1700.

The UI module 1710 may provide a specialized UI or a GUI thatinteroperates with the device 1000 for each application. The touchscreenmodule 1720 may detect a touch gesture of the user on a touchscreen andmay transmit information about the touch gesture to the controller 1300.The touchscreen module 1720 according to an embodiment may recognize andanalyze a touch code. The touchscreen module 1720 may be configured asseparate hardware including a controller.

Various sensors may be located in or near the touchscreen in order todetect a touch or a proximity touch of the touchscreen. An example of asensor for detecting a touch of the touchscreen may be a tactile sensorThe tactile sensor refers to a sensor that detects a contact of aspecific object to a degree that a person feels or a higher degree. Thetactile sensor may detect any of various information such as a roughnessof a contact surface, a rigidity of a contact object, or a temperatureof a contact point.

Also, an example of a sensor for detecting a touch of the touchscreenmay be a proximity sensor.

The proximity sensor refers to a sensor that detects an object that isapproaching a certain detection surface or a neighboring object by usingthe strength of an electromagnetic field or infrared rays without amechanical contact. Examples of the touch gesture of the user mayinclude a tap, a touch and hold, a double-tab, a drag, a panning, aflick, a drag and drop, and a swipe.

The notification module 1730 may generate a signal for notifying anevent occurring in the device 1000. The notification module 1730 mayoutput a notification signal as a video signal through the display 1210,may output a notification signal as an audio signal through the soundoutputter 1220, or may output a notification signal as a vibrationsignal through the vibration motor 1230.

FIG. 19 is a block diagram of a server 2000 according to an embodiment.

Referring to FIG. 19, the server 2000 according to an embodiment mayinclude a communicator 2100, a storage 2200, and a processor 2300.

The communicator 2100 may include one or more elements for communicatingwith the device 1000 and the content providing server 3000. For example,the communicator 2100 may include a short-range communicator, a mobilecommunicator, and a broadcast receiver. Also, the communicator 2100 maytransmit information used to provide recommended content based on adevice knowledge graph to/from the device 1000 and the content providingserver 3000.

The storage 2200 may store a program or instructions for processing andcontrolling the processor 2300 and may store data input to the server2000 or output from the server 2000. Also, the storage 2200 may store aserver knowledge graph. The storage 2200 may build a database of theserver knowledge graph for each category and may store the serverknowledge graph.

The processor 2300 (e.g., at least one processor) may generally controlan overall operation of the server 2000. For example, the processor 2300may control the communicator 2100 and the storage 2200 by executingprograms or instructions stored in the storage 2200. The processor 2300may control an operation of the server 2000 by controlling thecommunicator 2100 and the storage 2200.

In detail, the processor 2300 may generate the server knowledge graph.The processor 2300 may collect various kinds of big data from variousdevices and may generate the server knowledge graph by using thecollected big data. The big data collected by the processor 2300 may bedata abstracted from individual privacy information.

The processor 2300 may receive a request for the server knowledge graphfrom the device 1000. The processor 2300 may receive information about auser's profile and/or a category value from the device 1000. In thiscase, information about the user's privacy in the information about theuser's profile may be abstracted information.

The processor 2300 may provide the server knowledge graph to the device1000. The processor 2300 may extract the server knowledge graph to beprovided to the device 1000 from the storage 2200 in consideration ofthe user's profile and/or a category and may provide the extractedserver knowledge graph to the device 1000. In this case, the serverknowledge graph may be generated according to the user's profile and thecategory and may be stored in the storage 2200.

The processor 2300 may receive the device knowledge graph from whichprivacy information is removed from the device 1000. The processor 2300may update the server knowledge graph by using the device knowledgegraph from which the privacy information is removed. The processor 2300may update the server knowledge graph by inputting the server knowledgegraph and the device knowledge graph to a certain AI model. The updatedserver knowledge graph may later be used by the device 1000 to generatea second device knowledge graph.

An AI-related function according to embodiments is performed through aprocessor and a memory. The processor may include at least oneprocessor. In this case, the at least one processor may include ageneral-purpose processor such as a central processing unit (CPU), anaccess point (AP), or a digital signal processor (DSP), a graphicsprocessor such as a graphics processing unit (GPU) or a visionprocessing unit (VPU), or an AI processor such as a neural processingunit (NPU). The at least one processor controls input data to beprocessed according to a predefined operation rule or AI model stored ina memory. Alternatively, when the at least one processor is an AIprocessor, the AI processor may be designed to have a hardware structurespecialized to process a specific AI model.

The predefined operation rule or AI model is created through learning.When the predefined operation rule or AI model is created throughlearning, it means that the predefined operation rule or AI model set toachieve desired characteristics (or purposes) is created when a basic AImodel is trained using a plurality of training data by using a learningalgorithm. The learning may be performed by a device itself in which AIaccording to embodiments is used, or may be performed through a separateserver and/or system. Examples of the learning algorithm may include,but are not limited to, supervised learning, unsupervised learning,semi-supervised learning, or reinforcement learning.

The AI mode may include a plurality of neural network layers. Theplurality of neural network layers have a plurality of weight values,and each perform a neural network operation through computation amongthe plurality of weight values and a computation result of a previouslayer. The plurality of weight values of the plurality of neural networklayers may be optimized by a training result of the AI model. Forexample, during a learning process, the plurality of weight values maybe updated to reduce or minimize a loss value or a cost value obtainedby the AI model. An artificial neural network may include a deep neuralnetwork (DNN), and examples of the artificial neural network mayinclude, but are not limited to, a convolutional neural network (CNN), aDNN, a recurrent neural network (RNN), a restricted Boltzmann machine(RBM), a deep belief network (DBN), a bidirectional recurrent deepneural network (BRDNN), and a deep Q-network.

Also, a first AI model may be generated by learning criteria forgenerating a first device knowledge graph. The first AI model may begenerated by learning criteria for determining which data is to begenerated or used to generate the first device knowledge graph and howto generate the first device knowledge graph by using the data.

Also, a second AI model may be generated by learning criteria forgenerating a second device knowledge graph. The second AI model may begenerated by learning criteria for determining which data is to be usedto generate the second device knowledge graph and how to generate thesecond device knowledge graph by using the data.

Also, a third AI model may be generated by learning criteria fordetermining recommended content. The third AI model may be generated bylearning criteria for determining which data is to be used to determinethe recommended content and how to determine the recommended content byusing the data.

The first AI model may output the first device knowledge graph by usingcertain data according to preset criteria as an input value. A resultantvalue output by the first AI model using the obtained data as an inputvalue may be used to update the first AI model.

Also, the second AI model may output the second device knowledge graphby using certain data according to preset criteria as an input value. Aresultant value output by a second AI model using the obtained data asan input value may be used to update the second AI model.

Furthermore, the third AI model may determine the recommended content byusing certain data according to preset criteria as an input value. Aresultant value output by the third AI model using the obtained data asan input value may be used to update the third AI model.

One or more embodiments may be implemented as a recording mediumincluding computer-executable instructions such as a program moduleexecuted by a computer. A computer-readable medium may be an arbitraryavailable medium accessible by a computer, and examples thereof includeall volatile and non-volatile media and separable and non-separablemedia. Further, examples of the computer-readable medium may include acomputer storage medium and a communication medium. Examples of thecomputer storage medium include all volatile and non-volatile media andseparable and non-separable media, which are implemented by an arbitrarymethod or technology, for storing information such as computer-readableinstructions, data structures, program modules, or other data. Thecommunication medium generally includes computer-readable instructions,data structures, program modules, or other data of a modulated datasignal.

Also, the term “unit” used herein may be a hardware component such as aprocessor or a circuit and/or a software component executed in ahardware component such as a processor.

While the disclosure has been particularly shown and described withreference to embodiments thereof, it will be understood by one ofordinary skill in the art that various changes in form and details maybe made therein without departing from the spirit and scope of thedisclosure as defined at least by the following claims. Hence, it willbe understood that the embodiments described above are not limiting ofthe scope of the disclosure. For example, each component described in asingle type may be executed in a distributed manner, and componentsdescribed distributed may also be executed in an integrated form.

The scope of the disclosure is indicated by at least the claims ratherthan by the detailed description, and it should be understood that theclaims and all modifications or modified forms drawn from the concept ofthe claims are included in the scope of the disclosure.

What is claimed is:
 1. A device for providing content based on a knowledge graph, the device comprising: a communication interface; a memory storing one or more instructions; and a processor configured to execute the one or more instructions to: obtain context information related to the device; obtain a first device knowledge graph of a user of the device by inputting the obtained context information to a first artificial intelligence (AI) model for determining a relation between entities related to the user of the device; control to request, from a server, a server knowledge graph generated by the server; control to receive the server knowledge graph from the server; obtain a second device knowledge graph of the user by inputting the obtained first device knowledge graph and the received server knowledge graph to a second AI model for extending the first device knowledge graph; and provide content based on the obtained second device knowledge graph.
 2. The device of claim 1, wherein each of the first AI model and the second AI model is an AI model trained by using, as an AI algorithm, at least one of a machine learning algorithm, a neural network algorithm, a genetic algorithm, a deep learning algorithm, or a classification algorithm.
 3. The device of claim 1, wherein the server knowledge graph is generated by the server based on big data provided to the server by the device and at least one other device.
 4. The device of claim 1, wherein the processor is further configured to execute the one or more instructions to process the context information into text indicating sequential operations and input the text into the first AI model.
 5. The device of claim 1, wherein: the processor is further configured to execute the one or more instructions to determine a privacy level for the first device knowledge graph and input the determined privacy level to the first AI model; and a part of data in the first device knowledge graph output from the first AI model comprises data abstracted according to the privacy level.
 6. The device of claim 1, wherein: the processor is further configured to execute the one or more instructions to input a category to the first AI model; and the first device knowledge graph corresponding to the input category is output from the first AI model.
 7. The device of claim 1, wherein the first AI model outputs the first device knowledge graph through at least one of entity extraction, entity analysis and abstraction, and relation extraction.
 8. The device of claim 1, wherein the processor is further configured to execute the one or more instructions to: control to transmit, to the server, information about a user profile of the user; and control to receive, from the server, the server knowledge graph related to the user profile.
 9. The device of claim 1, wherein the processor is further configured to execute the one or more instructions to control to transmit a certain category to the server and to receive, from the server, the server knowledge graph corresponding to the certain category.
 10. The device of claim 1, wherein the processor is further configured to execute the one or more instructions to: determine content to be recommended to the user by inputting, to a third AI model, operation information of the device and the second device knowledge graph; and control request a content providing server for the determined content to be recommended.
 11. A method, performed by a device, of providing content based on a knowledge graph, the method comprising: obtaining context information related to the device; obtaining a first device knowledge graph of a user of the device by inputting the obtained context information to a first artificial intelligence (AI) model for determining a relation between entities related to the user of the device; requesting, from a server, a server knowledge graph generated by the server; receiving, from the server, the server knowledge graph; obtaining a second device knowledge graph of the user by inputting the obtained first device knowledge graph and the received server knowledge graph to a second AI model for extending the first device knowledge graph; and providing content based on the obtained second device knowledge graph.
 12. The method of claim 11, wherein each of the first AI model and the second AI model is an AI model trained by using, as an AI algorithm, at least one of a machine learning algorithm, a neural network algorithm, a genetic algorithm, a deep learning algorithm, or a classification algorithm.
 13. The method of claim 11, wherein the server knowledge graph is generated by the server based on big data provided to the server by the device and at least one other device.
 14. The method of claim 11, further comprising: processing the context information into text indicating sequential operations, wherein the obtaining the first device knowledge graph comprises inputting the text to the first AI model.
 15. The method of claim 11, further comprising: determining a privacy level for the first device knowledge graph, wherein the obtaining the first device knowledge graph comprises inputting the privacy level to the first AI model, and wherein part of data in the first device knowledge graph output from the first AI model comprises data abstracted according to the privacy level.
 16. The method of claim 11, wherein: the obtaining the first device knowledge graph comprises inputting a category to the first AI model; and the first device knowledge graph corresponding to the input category is output from the first AI model.
 17. The method of claim 11, wherein the first AI model outputs the first device knowledge graph through at least one of entity extraction, entity analysis and abstraction, and relation extraction.
 18. The method of claim 11, wherein: the requesting the server knowledge graph from the server comprises transmitting, to the server, information about a user profile of the user; and the receiving the server knowledge graph comprises receiving, from the server, the server knowledge graph related to the user profile.
 19. The method of claim 11, wherein: the requesting the server knowledge graph from the server comprises transmitting a certain category to the server; and the receiving the server knowledge graph comprises receiving, from the server, the server knowledge graph corresponding to the certain category.
 20. A non-transitory computer-readable recording medium having recorded thereon a program executable by at least one processor to perform: obtaining context information related to a device; obtaining a first device knowledge graph of a user of the device by inputting the obtained context information to a first artificial intelligence (AI) model for determining a relation between entities related to the user of the device; controlling to request, from a server, a server knowledge graph generated by the server; controlling to receive, from the server, the server knowledge graph; obtaining a second device knowledge graph of the user by inputting the obtained first device knowledge graph and the received server knowledge graph to a second AI model for extending the first device knowledge graph; and providing content based on the obtained second device knowledge graph. 